Diegolden

Topic 6: Autoscaling

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Topic 6: Autoscaling

Official baseline: (The Kubernetes Authors, 2026b, 2026a). My working version: Autoscaling is a feedback loop. It needs metrics, resource requests, and a workload that can survive replica changes.

Mental Model

Autoscaling is a feedback loop. It needs metrics, resource requests, and a workload that can survive replica changes.

Notes

  • Horizontal scaling changes replica count; vertical scaling changes resource shape.
  • Bad requests create bad scaling decisions.
  • Scale-to-zero is not the default Kubernetes mental model.

Homelab Angle

Autoscaling in a homelab is mostly education unless you have variable load and enough spare capacity.

Verify It

  • Read the object status before changing the manifest.
  • Check events for the controller or node that is actually complaining.
  • Confirm the official source linked below still matches the cluster version you run.

Common Failure Modes

  • Treating the YAML object as the system, instead of one input to a reconciliation loop.
  • Debugging from outside the cluster when the failure only exists inside cluster networking or node state.
  • Forgetting that Kubernetes version, addon version, and runtime behavior are linked.

Sources

  • Workload Autoscaling - source path: content/en/docs/concepts/workloads/autoscaling.md, commit 8cc9e19b8eec8d5cf49eacd66f86a81648edb1a0.
  • Resource Management for Pods and Containers - source path: content/en/docs/concepts/configuration/manage-resources-containers.md, commit 8cc9e19b8eec8d5cf49eacd66f86a81648edb1a0.
  • Kubernetes documentation is licensed under CC BY 4.0; these notes are original commentary and link back to the official source.
The Kubernetes Authors. (2026a). Resource Management for Pods and Containers. https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/
The Kubernetes Authors. (2026b). Workload Autoscaling. https://kubernetes.io/docs/concepts/workloads/autoscaling/

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